Delayed Impact of Fair Machine Learning · HackerPapers discussions

Delayed Impact of Fair Machine Learning — mentioned by lightcatcher

Fri, 04 Dec 2020 00:50:00 GMT

Fairness, Accountability, Transparency (aka FAT) is a real sub-field of machine learning, that is as technical as any other machine learning sub-field in my opinion. It is not a PR stunt. I've published at top ML conference a paper about some CUDA kernels I wrote to accelerate training a class of RNNs, so I feel have technical grounding to make these claims. Here are some FAT papers I've enjoyed: https://arxiv.org/abs/1806.08010 https://arxiv.org/abs/1802.04023 https://arxiv.org/abs/1803.04383 (won best paper award at ICML 2018) Many FAT papers are published at NeurIPS or ICML (generally…

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Delayed Impact of Fair Machine Learning — mentioned by google234123

Sun, 27 Sep 2020 01:59:52 GMT

Reducing accuracy to try and improve fairness is definitely the politically correct thing to do. There was a good paper on this topic about how trying to make a model more fair can result in worst outcomes: https://arxiv.org/abs/1803.04383 If some group does happen to have a higher recidivism risk, pretending this isn't true and just correcting against that fact will just result in more crime.

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Delayed Impact of Fair Machine Learning — mentioned by yorwba

Wed, 23 Jan 2019 20:51:32 GMT

> The problem is the self-reinforcing, external factors that these algorithms don't take into account. And changing the algorithms isn't the right way to fix those external factors. If you try, their self-reinforcing nature is going to cause exactly the opposite of what you want. To stay with the crime vs. race example, assume that the ratio of black to white criminals is greater than you'd expect from the ratio of blacks to whites in the overall population, and that is due to self-reinforcing external factors. Your algorithm for deciding whether to parole a prisoner picks up on that…

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